pkg_list <- c("sf", "terra", "ReLTER", "tmap")
lapply(pkg_list, require, character.only = TRUE)
## [[1]] ## [1] TRUE ## ## [[2]] ## [1] TRUE ## ## [[3]] ## [1] TRUE ## ## [[4]] ## [1] TRUE
tmap_options(check.and.fix = TRUE)
tmap_mode("view")
13/04/2022
pkg_list <- c("sf", "terra", "ReLTER", "tmap")
lapply(pkg_list, require, character.only = TRUE)
## [[1]] ## [1] TRUE ## ## [[2]] ## [1] TRUE ## ## [[3]] ## [1] TRUE ## ## [[4]] ## [1] TRUE
tmap_options(check.and.fix = TRUE)
tmap_mode("view")
ReLTER relies on the data entered into DEIMS-SDR. However sometimes there are:
Query for Site Manager
# Multiple sites in the KISKUN region of Hungary
kiskun <- get_ilter_generalinfo(country_name = "Hungary",
site_name = "KISKUN")
# How many sites?
print(paste("In Kiskun region: ", length(kiskun$title), "sites"))
## [1] "In Kiskun region: 8 sites"
(kiskun$title)
## [1] "Kiskun Forest Reserve Sites, KISKUN LTER - Hungary" ## [2] "VULCAN Kiskunsag, KISKUN LTER - Hungary" ## [3] "Kiskun Restoration Experiments, KISKUN LTER - Hungary" ## [4] "Kiskun Site Network (Jedlik), KISKUN LTER - Hungary" ## [5] "KISKUN LTER - Hungary" ## [6] "LTER Fulophaza Site, KISKUN LTER - Hungary" ## [7] "Bugac-Bocsa-Orgovany Site, KISKUN LTER - Hungary" ## [8] "Orgovany Site, KISKUN LTER - Hungary"
# Which site? Bugac-Bocsa bugac_id <- kiskun[7,]$uri bugac_details <- get_site_info(bugac_id,"Contacts") (bugac_details$generalInfo.siteManager[[1]]['name'])
## name ## 1 Gábor Ónodi
Now query for boundary
bugac_polygon <- get_site_info(bugac_id, "Boundaries")
## ## ---- ## This site does not have boundaries uploaded to DEIMS-SDR. ## Please verify in the site page: https://deims.org/609e5959-8cd8-44a0-ab42-eda521cd452a ## ----
str(bugac_polygon)
## tibble [1 × 9] (S3: tbl_df/tbl/data.frame) ## $ title : chr "Bugac-Bocsa-Orgovany Site, KISKUN LTER - Hungary" ## $ uri : chr "https://deims.org/609e5959-8cd8-44a0-ab42-eda521cd452a" ## $ boundaries : logi NA ## $ geoCoord : chr "POINT (19.5281 46.7183)" ## $ country :List of 1 ## ..$ : chr "Hungary" ## $ geoElev.avg : int 112 ## $ geoElev.min : int 105 ## $ geoElev.max : int 120 ## $ geoElev.unit: chr "msl"
# No geometry
paradiso <- get_ilter_generalinfo(country_name = "Italy",
site_name = "Gran Paradiso")
(paradiso$title)
## [1] "IT23 - Gran Paradiso National Park - Italy" ## [2] "Gran Paradiso National Park - Italy"
# Choose the second paradiso_id <- paradiso[2,]$uri paradiso_details <- get_site_info(paradiso_id,"Contacts") # Multiple names for metadata: paradiso_details$generalInfo.metadataProvider[[1]]['name']
## name ## 1 Alessandro Oggioni ## 2 Ramona Viterbi
# But what about funding agency paradiso_details$generalInfo.fundingAgency
## [1] NA
Functions within ReLTER help to acquire certain Earth Observation datasets. The get_site_ODS() function offers to ReLTER users access to the OpenDataScience Europe (ODS) archive (https://maps.opendatascience.eu/) with landcover, NDVI, natura2000, Corine landcover, and OSM features, all at 30 meter pixel resolution. Cropping to site boundaries is done in the cloud, and due to the Cloud Optimized Geotiff (COG) format, downloads are quite small.
# Get DEIMS ID for Kis-Balaton site
kis_balaton <- get_ilter_generalinfo(country_name = "Hungary",
site_name = "Kis-Balaton")
kb_id = kis_balaton$uri
kb_polygon = get_site_info(kb_id, "Boundaries")
# Now acquire landcover and NDVI from ODS
kb_landcover = get_site_ODS(kb_id, dataset = "landcover")
kb_ndvi_summer = get_site_ODS(kb_id, "ndvi_summer")
# Plot maps
tm_basemap("OpenStreetMap.Mapnik") +
tm_shape(kb_polygon) +
tm_borders(col = "purple") +
tm_shape(kb_ndvi_summer) +
tm_raster(alpha=0.7, palette = "RdYlGn")
tm_basemap("OpenStreetMap.Mapnik") +
tm_shape(kb_polygon) +
tm_borders(col = "purple") +
tm_shape(kb_landcover) +
tm_raster(alpha=0.7, palette = "Set1")
lezirias <- get_ilter_generalinfo(country_name = "Portugal",
site_name = "Companhia")
lezirias_id = lezirias$uri
lezirias_polygon = get_site_info(lezirias_id, "Boundaries")
# Now acquire spring NDVI from OSD
lezirias_ndvi_spring = get_site_ODS(lezirias_id, "ndvi_spring")
# Plot maps
tm_basemap("OpenStreetMap.Mapnik") +
tm_shape(lezirias_polygon) +
tm_borders(col = "purple") +
tm_shape(lezirias_ndvi_spring) +
tm_raster(alpha=0.7, palette = "RdYlGn")
The function outputs a raster. We can save to Geotiff for use in other GIS
class(lezirias_ndvi_spring)
## [1] "SpatRaster" ## attr(,"package") ## [1] "terra"
writeRaster(x = lezirias_ndvi_spring,
filename = "lezirias_ndvi_spring.tif",
overwrite = TRUE)
ReLTER has implemented some revealing visualizations of the various parameters collected at LTER sites. One visualization is the pie chart of environmental parameters.
In an example above the DEIMS ID of Kis Balaton (Kiskun LTER) was found. We’ll use that site to show a pie chart of environmental variables collected in that site.
produce_site_parameters_pie(kb_id)
## # A tibble: 8 × 9 ## parameterGroups n freq label end start middle hjust vjust ## <chr> <int> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 agricultural parameter 1 0.0208 2% 0.131 0 0.0654 0 0 ## 2 atmospheric parameter 1 0.0208 2% 0.262 0.131 0.196 0 0 ## 3 biological parameter 16 0.333 33% 2.36 0.262 1.31 0 0 ## 4 chemical parameter 16 0.333 33% 4.45 2.36 3.40 1 1 ## 5 ecosystem parameter 8 0.167 17% 5.50 4.45 4.97 1 0 ## 6 physical parameter 1 0.0208 2% 5.63 5.50 5.56 1 0 ## 7 soil parameter 1 0.0208 2% 5.76 5.63 5.69 1 0 ## 8 water parameter 4 0.0833 8% 6.28 5.76 6.02 1 0
Similarly, a “waffle” chart can be produced.
produce_site_parameters_waffle(kb_id)
## # A tibble: 8 × 4 ## parameterGroups n freq label ## <chr> <int> <dbl> <chr> ## 1 agricultural parameter 1 0.0208 2% ## 2 atmospheric parameter 1 0.0208 2% ## 3 biological parameter 16 0.333 33% ## 4 chemical parameter 16 0.333 33% ## 5 ecosystem parameter 8 0.167 17% ## 6 physical parameter 1 0.0208 2% ## 7 soil parameter 1 0.0208 2% ## 8 water parameter 4 0.0833 8%
This example uses the LTER network in Greece. Call the produce_network_points_map() function (requires both DEIMS network ID and the three letter ISO code for the country to be mapped) to get all sites in a country.
lter_greece_id = "https://deims.org/networks/83453a6c-792d-4549-9dbb-c17ced2e0cc3"
lter_greece <- produce_network_points_map(lter_greece_id, "GRC")
grc <- readRDS("gadm36_GRC_0_sp.rds") # available from `produce_network_points_map()
tm_basemap("OpenStreetMap.Mapnik") +
tm_shape(lter_greece) +
tm_dots(col = "blue", size=0.08) +
tm_shape(grc) +
tm_borders(col = "purple", lwd=2) +
tm_grid(alpha = 0.4) +
tm_scale_bar(position = c("right", "bottom"))